store = {}
store['args']={'name': 'emnist_multibald_bald_k10_335690', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 335690, 'acquisition_method': 'AcquisitionMethod.multibald', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 13, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=13', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.020106382978723403, 'nll': 3.8618894968641566}, 'chosen_samples': [66753, 10002, 109990, 93147, 3849], 'chosen_samples_score': [0.012648114969007196, 0.02523561581485545, 0.0375350388731146, 0.05290383169104729, 0.06601846904284159], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.03590425531914894, 'nll': 49.98275194857983}, 'chosen_samples': [43564, 12487, 14822, 39482, 35241], 'chosen_samples_score': [1.1782063047315217, 1.8611860483073426, 2.1843508352506995, 2.248765912071603, 2.298831234104998], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.08382978723404255, 'nll': 36.72770124394843}, 'chosen_samples': [8267, 85103, 5409, 5899, 15834], 'chosen_samples_score': [1.2781708688536773, 1.964160010927567, 2.1805830768637637, 2.25752511330212, 2.2822632756081465], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.09872340425531916, 'nll': 37.92284732575112}, 'chosen_samples': [105059, 77424, 46548, 64537, 43951], 'chosen_samples_score': [1.2555265983576664, 1.935302194011684, 2.160702757291514, 2.217592541923795, 2.282879834018352], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.12340425531914893, 'nll': 35.20970633648812}, 'chosen_samples': [4834, 58344, 105898, 43548, 92473], 'chosen_samples_score': [1.38364227281724, 2.135216306526882, 2.2592965284844095, 2.2934539439568122, 2.2936326619525707], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.15420212765957447, 'nll': 29.647674083303897}, 'chosen_samples': [29465, 96907, 65344, 72372, 35301], 'chosen_samples_score': [1.5793976737486086, 2.1465506699772847, 2.263760325389891, 2.256082326665914, 2.29436548129962], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.13792553191489362, 'nll': 25.498242382293054}, 'chosen_samples': [40156, 61961, 29832, 65575, 42562], 'chosen_samples_score': [1.4622784858159008, 2.1493904120274654, 2.2636232047173306, 2.2831490364634384, 2.317722674182722], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.16287234042553192, 'nll': 28.542258810489734}, 'chosen_samples': [463, 27632, 97695, 39078, 106576], 'chosen_samples_score': [1.6367959878684513, 2.185731159043736, 2.289535156984668, 2.314304407569089, 2.294638669157757], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.16382978723404254, 'nll': 24.090674519640334}, 'chosen_samples': [49874, 53798, 101115, 80407, 12480], 'chosen_samples_score': [1.6011199793281112, 2.1722040254321313, 2.276159250428646, 2.307978773351273, 2.30359446630654], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1875, 'nll': 22.525360274619246}, 'chosen_samples': [68857, 19017, 21328, 59397, 107129], 'chosen_samples_score': [1.6806731267787407, 2.2158261475302297, 2.283302727967471, 2.3129321098104194, 2.289382782853739], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.2021808510638298, 'nll': 21.328145909410843}, 'chosen_samples': [81078, 18201, 103283, 55196, 11410], 'chosen_samples_score': [1.7957017858536466, 2.281412075400124, 2.3001404863809176, 2.3069574096478074, 2.29291183974876], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.18925531914893617, 'nll': 21.338162763879655}, 'chosen_samples': [110649, 9530, 78317, 35870, 6353], 'chosen_samples_score': [1.676237478924043, 2.2308355490806107, 2.2914086071976847, 2.309890492249794, 2.2605653958097043], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23382978723404255, 'nll': 16.75679033644656}, 'chosen_samples': [6645, 87730, 98036, 675, 12765], 'chosen_samples_score': [1.4739757699699332, 2.050808256384194, 2.234917621163859, 2.289064899998074, 2.2996187860653023], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21430851063829787, 'nll': 17.772470144718252}, 'chosen_samples': [17590, 59758, 33733, 64854, 73265], 'chosen_samples_score': [1.636456436225335, 2.2201089706548482, 2.2910500205049997, 2.30487115925317, 2.3015323370124356], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.24765957446808512, 'nll': 18.28500917637602}, 'chosen_samples': [26326, 86515, 60652, 42610, 22576], 'chosen_samples_score': [1.5809244095409278, 2.216242274463673, 2.2874104832702336, 2.28493451012019, 2.316384154150497], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23085106382978723, 'nll': 16.57808075925137}, 'chosen_samples': [42474, 14372, 106686, 83614, 237], 'chosen_samples_score': [1.7571166995659975, 2.2215461141007316, 2.2891593027650763, 2.309843596784991, 2.3226278190019016], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.265, 'nll': 15.065311949709628}, 'chosen_samples': [78404, 98625, 104529, 69235, 48665], 'chosen_samples_score': [1.5928726315282562, 2.190151738252031, 2.2790450689096238, 2.2933582055292665, 2.3036763314081834], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2806382978723404, 'nll': 13.417674283778414}, 'chosen_samples': [27401, 2006, 106765, 15539, 44813], 'chosen_samples_score': [1.487900517583668, 2.162937574736703, 2.2744743510741734, 2.2739172727148524, 2.2967041872108], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28952127659574467, 'nll': 13.06496156651923}, 'chosen_samples': [33042, 112001, 93565, 61874, 95626], 'chosen_samples_score': [1.6953676674538436, 2.19861375353358, 2.285367920951517, 2.2598583604655884, 2.294799442845096], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.28308510638297874, 'nll': 13.95458264371182}, 'chosen_samples': [94785, 15894, 71110, 55621, 67256], 'chosen_samples_score': [1.6244502257811715, 2.2501187967258005, 2.294852307536038, 2.2772130209228507, 2.2927654954040895], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.29436170212765955, 'nll': 13.159816109677578}, 'chosen_samples': [89, 31725, 56028, 17436, 75659], 'chosen_samples_score': [1.6818253327385806, 2.2063018952696485, 2.286973002971722, 2.2795709142725116, 2.295896150401862], 'chosen_samples_orignal_score': None})
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